Improving the performance of self-organized robotic clustering: modeling and planning sequential changes to the division of labor Conference Paper uri icon

abstract

  • Robotic clustering involves gathering spatially distributed objects into a single pile. It is a canonical task for self-organized multi-robot systems: several authors have proposed and demonstrated algorithms for performing the task. In this paper, we consider a setting in which heterogeneous strategies outperform homogeneous ones and changing the division of labor can improve performance. By modeling the clustering dynamics with a Markov chain model, we are able to predict performance of the task by different divisions of labor. We propose and demonstrate a method that is able to select an open-loop sequence of changes to the division of labor, based on this stochastic model, that increases performance. We validate our proposed method on physical robot experiments. 2013 IEEE.

name of conference

  • 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

published proceedings

  • 2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)

author list (cited authors)

  • Kim, J., & Shell, D. A.

citation count

  • 1

complete list of authors

  • Kim, Jung-Hwan||Shell, Dylan A

publication date

  • November 2013